Sparse time-frequency representation via atomic norm minimization

Tsubasa Kusano, Kohei Yatabe, Yasuhiro Oikawa

Research output: Contribution to journalConference articlepeer-review

Abstract

Nonstationary signals are commonly analyzed and processed in the time-frequency (T-F) domain that is obtained by the discrete Gabor transform (DGT). The T-F representation obtained by DGT is spread due to windowing, which may degrade the performance of T-F domain analysis and processing. To obtain a well-localized T-F representation, sparsity-aware methods using '1-norm have been studied. However, they need to discretize a continuous parameter onto a grid, which causes a model mismatch. In this paper, we propose a method of estimating a sparse T-F representation using atomic norm. The atomic norm enables sparse optimization without discretization of continuous parameters. Numerical experiments show that the T-F representation obtained by the proposed method is sparser than the conventional methods.

Original languageEnglish
Pages (from-to)5075-5079
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 2021 Jun 62021 Jun 11

Keywords

  • Atomic norm
  • Basis pursuit
  • Convex optimization
  • Semidefinite programming
  • Short-time fourier transform (STFT)

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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